Stress-related cellular pathophysiology as a crosstalk risk factor for neurocognitive and psychiatric disorders
Bibliographic record
Abstract
In this narrative review, we examine biological processes linking psychological stress and cognition, with a focus on how psychological stress can activate multiple neurobiological mechanisms that drive cognitive decline and behavioral change. First, we describe the general neurobiology of the stress response to define neurocognitive stress reactivity. Second, we review aspects of epigenetic regulation, synaptic transmission, sex hormones, photoperiodic plasticity, and psychoneuroimmunological processes that can contribute to cognitive decline and neuropsychiatric conditions. Third, we explain mechanistic processes linking the stress response and neuropathology. Fourth, we discuss molecular nuances such as an interplay between kinases and proteins, as well as differential role of sex hormones, that can increase vulnerability to cognitive and emotional dysregulation following stress. Finally, we explicate several testable hypotheses for stress, neurocognitive, and neuropsychiatric research. Together, this work highlights how stress processes alter neurophysiology on multiple levels to increase individuals' risk for neurocognitive and psychiatric disorders, and points toward novel therapeutic targets for mitigating these effects. The resulting models can thus advance dementia and mental health research, and translational neuroscience, with an eye toward clinical application in cognitive and behavioral neurology, and psychiatry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".